Nearest neighbour approaches for Emotion Detection in Tweets
This provides an explainable alternative to deep learning for emotion detection in social media, though it is incremental as it applies an existing method to a known task.
The paper tackles emotion detection in tweets by proposing a weighted k-nearest neighbors approach, which achieves results competitive with state-of-the-art methods while offering improved interpretability.
Emotion detection is an important task that can be applied to social media data to discover new knowledge. While the use of deep learning methods for this task has been prevalent, they are black-box models, making their decisions hard to interpret for a human operator. Therefore, in this paper, we propose an approach using weighted $k$ Nearest Neighbours (kNN), a simple, easy to implement, and explainable machine learning model. These qualities can help to enhance results' reliability and guide error analysis. In particular, we apply the weighted kNN model to the shared emotion detection task in tweets from SemEval-2018. Tweets are represented using different text embedding methods and emotion lexicon vocabulary scores, and classification is done by an ensemble of weighted kNN models. Our best approaches obtain results competitive with state-of-the-art solutions and open up a promising alternative path to neural network methods.